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# A95329 Statistics

Scuola di Economia e Management
Syllabus
Academic Year 2017/18 First Semester Docente Titolare Marta Nai Ruscone E-mail mnairuscone@liuc.it Office "Edificio 1" (in front of main tower), ground floor Phone 0331 572330

Learning Objectives

The course deals with concepts, methods and techniques for analyzing data, first from a data-descriptive perspective, and then from an inferential viewpoint. The course will also provide basic ideas of probability theory to evaluate uncertainty statements. The focus is on analyzing real data, using software tools.

Learning targets

1. Demonstrate the ability to apply fundamental concepts in exploratory data analysis.

2. Demonstrate an understanding of the basic concepts of probability and random variables.

3. Understand the concept of the sampling distribution of a statistic, and in particular describe the behaviour of the sample mean.

4. Understand the foundations for classical inference involving confidence intervals and hypothesis testing.

5. Apply inferential methods relating to the means of Normal distributions.

6. Apply and interpret basic summary and modelling techniques for bivariate data and use inferential methods in the context of simple linear models with Normally distributed errors.

7. Interpret and analyse data that may be displayed in a two—way table.

8. Apply and interpret simple and multiple linear regression model: explanatory power of the model, parameter estimation, forecasting.

Course Content

–    Describing data: frequency distributions, graphical representation, measures of location and spread.

–    Two-way frequency tables, scatterplots, and measures of dependence (covariance, correlation coefficient).

–    Probability: events, rules of probability, discrete and continuous random variables.

–    Sampling and sampling distributions: sampling mean, proportion and variance.

–    Inference: point estimation (statistic, main properties); confidence interval; hypothesis testing: single population and two-populations.

–    Simple and multiple linear regression model: explanatory power of the model, parameter estimation, forecasting.

Course Delivery

The course involves lectures and exercise sessions using PC-labs. Active participation, ongoing personal study and self-evaluation through the online platform MyMathLab Global are required.

Course Evaluation

The exam consists of a written general test.

Students who systematically attend the course lectures are allowed to replace the general written test with two written partial tests.

Syllabus

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